{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of outcome')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.Outcome)\n",
    "plt.xlabel('outcome')\n",
    "plt.ylabel('Number of outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Pregnancies')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.Pregnancies)\n",
    "plt.xlabel('Pregnancies')\n",
    "plt.ylabel('Number of Pregnancies')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Glucose')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.Glucose)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of Glucose')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of BloodPressure')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.BloodPressure)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Number of BloodPressure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of SkinThickness')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.SkinThickness)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('Number of SkinThickness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bd7c4d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'SkinThickness',hue = 'Outcome',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b6a2dd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Age',hue = 'Outcome',data=train)\n",
    "# 年龄大些患病比例增加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c052ed0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'BMI',hue = 'Outcome',data=train)\n",
    "#BMI 越低患病更少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
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       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Outcome   0\n",
       "0    21        0  58\n",
       "1    21        1   5\n",
       "2    22        0  61\n",
       "3    22        1  11\n",
       "4    23        0  31\n",
       "5    23        1   7\n",
       "6    24        0  38\n",
       "7    24        1   8\n",
       "8    25        0  34\n",
       "9    25        1  14\n",
       "10   26        0  25\n",
       "11   26        1   8\n",
       "12   27        0  24\n",
       "13   27        1   8\n",
       "14   28        0  25\n",
       "15   28        1  10\n",
       "16   29        0  16\n",
       "17   29        1  13\n",
       "18   30        0  15\n",
       "19   30        1   6\n",
       "20   31        0  11\n",
       "21   31        1  13\n",
       "22   32        0   7\n",
       "23   32        1   9\n",
       "24   33        0   7\n",
       "25   33        1  10\n",
       "26   34        0  10\n",
       "27   34        1   4\n",
       "28   35        0   5\n",
       "29   35        1   5\n",
       "30   36        0   6\n",
       "31   36        1  10\n",
       "32   37        0  13\n",
       "33   37        1   6\n",
       "34   38        0   6\n",
       "35   38        1  10\n",
       "36   39        0   9\n",
       "37   39        1   3\n",
       "38   40        0   7\n",
       "39   40        1   6\n",
       "40   41        0   9\n",
       "41   41        1  13\n",
       "42   42        0  11\n",
       "43   42        1   7\n",
       "44   43        0   2\n",
       "45   43        1  11\n",
       "46   44        0   3\n",
       "47   44        1   5\n",
       "48   45        0   7\n",
       "49   45        1   8\n",
       "50   46        0   6\n",
       "51   46        1   7\n",
       "52   47        0   2\n",
       "53   47        1   4\n",
       "54   48        0   4\n",
       "55   48        1   1\n",
       "56   49        0   2\n",
       "57   49        1   3\n",
       "58   50        0   3\n",
       "59   50        1   5\n",
       "60   51        0   3\n",
       "61   51        1   5\n",
       "62   52        0   1\n",
       "63   52        1   7\n",
       "64   53        0   1\n",
       "65   53        1   4\n",
       "66   54        0   2\n",
       "67   54        1   4\n",
       "68   55        0   3\n",
       "69   55        1   1\n",
       "70   56        0   1\n",
       "71   56        1   2\n",
       "72   57        0   4\n",
       "73   57        1   1\n",
       "74   58        0   4\n",
       "75   58        1   3\n",
       "76   59        0   1\n",
       "77   59        1   2\n",
       "78   60        0   3\n",
       "79   60        1   2\n",
       "80   61        0   1\n",
       "81   61        1   1\n",
       "82   62        0   2\n",
       "83   62        1   2\n",
       "84   63        0   4\n",
       "85   64        0   1\n",
       "86   65        0   3\n",
       "87   66        0   2\n",
       "88   66        1   2\n",
       "89   67        0   2\n",
       "90   67        1   1\n",
       "91   68        0   1\n",
       "92   69        0   2\n",
       "93   70        1   1\n",
       "94   72        0   1\n",
       "95   81        0   1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 100)\n",
    "train.groupby(by=['Age','Outcome']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Pregnancies</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Glucose</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BloodPressure</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SkinThickness</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Insulin</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BMI</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DiabetesPedigreeFunction</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Outcome</td>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  DiabetesPedigreeFunction       Age   Outcome\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672 -0.073535  0.017683                 -0.033523  0.544341  0.221898\n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328  0.331357  0.221071                  0.137337  0.263514  0.466581\n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371  0.088933  0.281805                  0.041265  0.239528  0.065068\n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000  0.436783  0.392573                  0.183928 -0.113970  0.074752\n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783  1.000000  0.197859                  0.185071 -0.042163  0.130548\n",
       "BMI                          0.017683  0.221071       0.281805       0.392573  0.197859  1.000000                  0.140647  0.036242  0.292695\n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928  0.185071  0.140647                  1.000000  0.033561  0.173844\n",
       "Age                          0.544341  0.263514       0.239528      -0.113970 -0.042163  0.036242                  0.033561  1.000000  0.238356\n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752  0.130548  0.292695                  0.173844  0.238356  1.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d1d2b10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Glucose',hue = 'Outcome',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e6c0cd0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 111\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4.423177</td>\n",
       "      <td>121.656250</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>29.108073</td>\n",
       "      <td>140.671875</td>\n",
       "      <td>32.455208</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.980481</td>\n",
       "      <td>30.438286</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>8.791221</td>\n",
       "      <td>86.383060</td>\n",
       "      <td>6.875177</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin         BMI  DiabetesPedigreeFunction         Age     Outcome\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000  768.000000                768.000000  768.000000  768.000000\n",
       "mean      4.423177  121.656250      72.386719      29.108073  140.671875   32.455208                  0.471876   33.240885    0.348958\n",
       "std       2.980481   30.438286      12.096642       8.791221   86.383060    6.875177                  0.331329   11.760232    0.476951\n",
       "min       1.000000   44.000000      24.000000       7.000000   14.000000   18.200000                  0.078000   21.000000    0.000000\n",
       "25%       2.000000   99.750000      64.000000      25.000000  121.500000   27.500000                  0.243750   24.000000    0.000000\n",
       "50%       4.000000  117.000000      72.000000      29.000000  125.000000   32.300000                  0.372500   29.000000    0.000000\n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   36.600000                  0.626250   41.000000    1.000000\n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   67.100000                  2.420000   81.000000    1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对为0的特征进行填充,然后做tfidf,然后标准化\n",
    "NaN_col_names = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())\n",
    "medians = train.median()\n",
    "train = train.fillna(medians)\n",
    "print(train.isnull().sum())\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  Age  Outcome\n",
       "0          6.0    148.0           72.0           35.0    125.0  33.6                     0.627   50        1\n",
       "1          1.0     85.0           66.0           29.0    125.0  26.6                     0.351   31        0\n",
       "2          8.0    183.0           64.0           29.0    125.0  23.3                     0.672   32        1\n",
       "3          1.0     89.0           66.0           23.0     94.0  28.1                     0.167   21        0\n",
       "4          4.0    137.0           40.0           35.0    168.0  43.1                     2.288   33        1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
       "0          6.0    148.0           72.0           35.0    125.0  33.6                     0.627   50\n",
       "1          1.0     85.0           66.0           29.0    125.0  26.6                     0.351   31\n",
       "2          8.0    183.0           64.0           29.0    125.0  23.3                     0.672   32\n",
       "3          1.0     89.0           66.0           23.0     94.0  28.1                     0.167   21\n",
       "4          4.0    137.0           40.0           35.0    168.0  43.1                     2.288   33"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = train['Outcome']\n",
    "x_train = train.drop(['Outcome'],axis=1)\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_name = x_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies_tfidf</th>\n",
       "      <th>Glucose_tfidf</th>\n",
       "      <th>BloodPressure_tfidf</th>\n",
       "      <th>SkinThickness_tfidf</th>\n",
       "      <th>Insulin_tfidf</th>\n",
       "      <th>BMI_tfidf</th>\n",
       "      <th>DiabetesPedigreeFunction_tfidf</th>\n",
       "      <th>Age_tfidf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.027500</td>\n",
       "      <td>0.678333</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.160417</td>\n",
       "      <td>0.572916</td>\n",
       "      <td>0.154000</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>0.229167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.005801</td>\n",
       "      <td>0.493082</td>\n",
       "      <td>0.382863</td>\n",
       "      <td>0.168228</td>\n",
       "      <td>0.725120</td>\n",
       "      <td>0.154306</td>\n",
       "      <td>0.002036</td>\n",
       "      <td>0.179830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.033902</td>\n",
       "      <td>0.775519</td>\n",
       "      <td>0.271220</td>\n",
       "      <td>0.122896</td>\n",
       "      <td>0.529726</td>\n",
       "      <td>0.098741</td>\n",
       "      <td>0.002848</td>\n",
       "      <td>0.135610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.006612</td>\n",
       "      <td>0.588467</td>\n",
       "      <td>0.436392</td>\n",
       "      <td>0.152076</td>\n",
       "      <td>0.621527</td>\n",
       "      <td>0.185797</td>\n",
       "      <td>0.001104</td>\n",
       "      <td>0.138852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.017410</td>\n",
       "      <td>0.596296</td>\n",
       "      <td>0.174101</td>\n",
       "      <td>0.152338</td>\n",
       "      <td>0.731224</td>\n",
       "      <td>0.187594</td>\n",
       "      <td>0.009959</td>\n",
       "      <td>0.143633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies_tfidf  Glucose_tfidf  BloodPressure_tfidf  SkinThickness_tfidf  Insulin_tfidf  BMI_tfidf  DiabetesPedigreeFunction_tfidf  Age_tfidf\n",
       "0           0.027500       0.678333             0.330000             0.160417       0.572916   0.154000                        0.002874   0.229167\n",
       "1           0.005801       0.493082             0.382863             0.168228       0.725120   0.154306                        0.002036   0.179830\n",
       "2           0.033902       0.775519             0.271220             0.122896       0.529726   0.098741                        0.002848   0.135610\n",
       "3           0.006612       0.588467             0.436392             0.152076       0.621527   0.185797                        0.001104   0.138852\n",
       "4           0.017410       0.596296             0.174101             0.152338       0.731224   0.187594                        0.009959   0.143633"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tfidf = TfidfTransformer()\n",
    "x_train_tfidf = tfidf.fit_transform(x_train).toarray()\n",
    "feat_names = columns_name + '_tfidf'\n",
    "x_train_tfidf = pd.DataFrame(columns=feat_names,data=x_train_tfidf)\n",
    "x_train_tfidf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ms_tfidf = MinMaxScaler()\n",
    "feat_name_tfidf = x_train_tfidf.columns\n",
    "x_train_tfidf = ms_tfidf.fit_transform(x_train_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies_tfidf</th>\n",
       "      <th>Glucose_tfidf</th>\n",
       "      <th>BloodPressure_tfidf</th>\n",
       "      <th>SkinThickness_tfidf</th>\n",
       "      <th>Insulin_tfidf</th>\n",
       "      <th>BMI_tfidf</th>\n",
       "      <th>DiabetesPedigreeFunction_tfidf</th>\n",
       "      <th>Age_tfidf</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.326047</td>\n",
       "      <td>0.724331</td>\n",
       "      <td>0.408045</td>\n",
       "      <td>0.390245</td>\n",
       "      <td>0.561096</td>\n",
       "      <td>0.372008</td>\n",
       "      <td>0.239969</td>\n",
       "      <td>0.494433</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.057579</td>\n",
       "      <td>0.443948</td>\n",
       "      <td>0.490637</td>\n",
       "      <td>0.411807</td>\n",
       "      <td>0.729136</td>\n",
       "      <td>0.372960</td>\n",
       "      <td>0.163360</td>\n",
       "      <td>0.370423</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.405261</td>\n",
       "      <td>0.871425</td>\n",
       "      <td>0.316210</td>\n",
       "      <td>0.286673</td>\n",
       "      <td>0.513412</td>\n",
       "      <td>0.199944</td>\n",
       "      <td>0.237597</td>\n",
       "      <td>0.259274</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.067613</td>\n",
       "      <td>0.588317</td>\n",
       "      <td>0.574267</td>\n",
       "      <td>0.367221</td>\n",
       "      <td>0.614765</td>\n",
       "      <td>0.471017</td>\n",
       "      <td>0.078123</td>\n",
       "      <td>0.267423</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.201212</td>\n",
       "      <td>0.600166</td>\n",
       "      <td>0.164476</td>\n",
       "      <td>0.367945</td>\n",
       "      <td>0.735875</td>\n",
       "      <td>0.476612</td>\n",
       "      <td>0.887961</td>\n",
       "      <td>0.279442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies_tfidf  Glucose_tfidf  BloodPressure_tfidf  SkinThickness_tfidf  Insulin_tfidf  BMI_tfidf  DiabetesPedigreeFunction_tfidf  Age_tfidf  Outcome\n",
       "0           0.326047       0.724331             0.408045             0.390245       0.561096   0.372008                        0.239969   0.494433        1\n",
       "1           0.057579       0.443948             0.490637             0.411807       0.729136   0.372960                        0.163360   0.370423        0\n",
       "2           0.405261       0.871425             0.316210             0.286673       0.513412   0.199944                        0.237597   0.259274        1\n",
       "3           0.067613       0.588317             0.574267             0.367221       0.614765   0.471017                        0.078123   0.267423        0\n",
       "4           0.201212       0.600166             0.164476             0.367945       0.735875   0.476612                        0.887961   0.279442        1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.Series(data=y_train,name='Outcome')\n",
    "train_tfidf = pd.concat([pd.DataFrame(columns= feat_name_tfidf,data= x_train_tfidf),y],axis=1)\n",
    "train_tfidf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tfidf.to_csv('diabetes_tfidf.csv',index=False,header=True)\n",
    "import _pickle as cPickle\n",
    "cPickle.dump(ms_tfidf,open('diabetes_MinMaxScaler_tfidf.pkl','wb'))\n",
    "cPickle.dump(tfidf,open('diabetes_tfidf.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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